class_docstr: Implements k-center-greedy method.
  
  Args:
      embedding (Tensor): Embedding vector extracted from a CNN
      sampling_ratio (float): Ratio to choose coreset size from the embedding size.
  
  Example:
      >>> embedding.shape
      paddle.Size([219520, 1536])
      >>> sampler = KCenterGreedy(embedding=embedding)
      >>> sampled_idxs = sampler.select_coreset_idxs()
      >>> coreset = embedding[sampled_idxs]
      >>> coreset.shape
      paddle.Size([219, 1536])
docstr: Reset minimum distances.
docstr_tok: ["Reset","minimum","distances","."]
code: def reset_distances(self) -> None:
      """Reset minimum distances."""
      self.min_distances = None
  
file_path: PaddleSeg/contrib/QualityInspector/qinspector/uad/utils/k_center_greedy.py
imports: from typing import List, Optional
  import paddle
  from paddle import Tensor
  from .random_projection import SparseRandomProjection
label_desc: Use image node_id 2 for calling the KCenterGreedy obj's underlying member method code with example usage: obj.reset_distances() without return types